Per-subject prediction and Jacobian for mixed-effects engines

Like the population gradient solver but takes a per-subject nsub x ntheta parameter matrix (phi = beta + b, as supplied by lme4::nlmer) instead of one shared theta. Requires .nlmSetupEnv() to already be loaded.

Usage

nlmerSolveGrad(thetaMat, record = FALSE)

Arguments

thetaMat

A nsub x ntheta matrix of per-subject parameter values. Row id is solved against subject id (in the loaded etTrans order).

record

When TRUE, record this evaluation's population parameter estimate – the per-subject mean of thetaMat's columns (phi = beta + b averaged over subjects, which equals the fixed effect exactly for parameters without a random effect) – into the resident nlm parameter history via the shared scale machinery. This is how an external optimizer (e.g. lme4::nlmer) populates the iteration print and the history recovered by nlmGetParHist(). No objective value is recorded (the scale's showOfv is expected to be 0 for these engines). Defaults to FALSE.

Value

A nobsTot x (ntheta+1) matrix in the loaded (etTrans) observation order: column 1 is the prediction (rx_pred_) and columns 2..(ntheta+1) are d(pred)/d(THETA[i]).

Details

This is an internal function and should not be called directly.

Author

Matthew L. Fidler